In network science, a small-world network is defined as a highly clustered network, thus showing a small distance (typically logarithmic in the number of network nodes) between any two nodes inside the network [1]}.
Considering the skip connections in neural networks,
we propose to use the small-world network concept to analyze networks with both short- and long-range (or skip) links. Indeed, small-world networks can be decomposed into: (i) a lattice network \(\mathcal {G}\)  accounting for short-range links; (ii) a random network \(\mathcal {R}\)  accounting for long-range links (see Fig. REF (c)).
The co-existence of a rich set of short- and long-range links leads to both a high degree of clustering and short average path length (logarithmic with network size).
We use the small-world network to model and analyze the topological property of neural networks in Section .
